International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019 Stakeholders and Criteria on a Mining Project using AHP and Entropy-Weight Methods

Solange Ríos, Alexi Delgado

Abstract: The abidance and evolution of mining industry for All this information is necessary as it is an effective means of the Peruvian government is of great relevance nowadays, due to rapprochement that will help to carry out assertive dialogues the economic impact, this entails in order to increase the gross [2]. “Stakeholder analysis is a process that seeks to identify domestic product and reduce the poverty index. Nevertheless, in and describe the interests and relationships of all the the last few years this production sector suffered numerous stakeholders in a given project” [3]. In general, this analysis problems because of the complicated social relationships with the is a necessary precondition to participatory planning and rural communities near the mining projects. The purpose of this study is to analyze the probability of a social conflict due to the project management. For this reason, it is very important, in appearance of a mining project in Macusani (Puno-Peru), using the planning stage, to determine the different existing the AHP (Analytic Hierarchy Process) methodology to evaluate stakeholders in the communities and the variables that could the stakeholders and the entropy-weight method for the criteria. In affect each one of them. order to accomplish the evaluation, we interview seven experts in The present scientific article, seeks to accurately identify community relationships that gave us their perspectives through the above mentioned using a quantitative method. Among the the polls. In terms of results, we determine that the stakeholders possible methods to use, we can mention the application of in order of importance were communal authorities, alpaca Delphi method [4] , AHP method and FAHP method [5], livestock, local and regional government, farmers and merchants. Grey Systems [6], Shannon entropy [7], Machine Learning By the other hand, the strongest variables in a social conflict were pollution, poverty level, employment, health, economy, water [8], among others. These methods make it possible to access and security. The analysis of this case of study can improve quantify the data obtained from qualitative research, this is new innovative techniques to affront the potential appearance of a because “qualitative data are presented in the form of social conflict, in order to create a social responsibility awareness. responses to standardized questionnaires” [9]. This can generate a positive impact expressed in benefits for the In this way, we can mention some social cases in which these local communities, the government and the enterprise. methods were applied. It is important to mention that many of them were not linked to mining. For example, in the Keywords: AHP, Entropy-weight, Lithium Mining project, international framework, we can mention a study carried out Stakeholders, Social Conflict. in an exploration of hydrocarbons in the Gulf of Valencia, Spain [10]. In this study, the use of ultrasound technology I. INTRODUCTION was proposed to determine the existence of hydrocarbon deposits in the subsoil. This proposal would generate an The social factor is becoming more important for the environmental impact, which is why Shannon's entropy was development of a mining project. Currently, one of the implemented with the objective of conducting environmental biggest obstacles that mining companies must overcome is conflict analysis (ECA). the social factor since it is the most difficult to predict and, On the other hand, in the national context, there is a therefore, to control. This is basically due to the difference in research carried out for the Conga Project in Cajamarca, in worldviews that both the rural sector and the urban sector which the entropy of Shannon and the grey clustering method have. This difference is most evident when it comes to were implemented to determine the ECA and the social gaining access to the surface terrain. This permission may be impact assessment (SIA), respectively. This study proved the inconvenient if not obtained, as "one of the fundamental efficiency of combining both methods for the evaluation of challenges for mining projects is to secure the consent of socio-environmental conflicts, since it provides very detailed local communities for future access to land, and to minimize results regarding the variables that are used for their the community's exposure to business uncertainty" [1]. resolution [11]. It is essential that there is a predisposition for understanding The case study for this article was the new lithium and and tolerance on both sides, which could be achieved by uranium project, Macusani, which is located in the province implementing an active and assertive dialogue. Such of Carabaya, district of Macusani in Puno department, Peru. dialogue would be feasible if there is a prior study of This project is in the exploration stage, and with only 15% of everything that encompasses the community. For instance, the surface drilled, 2.5 million tons of lithium and 124 million the analysis of traditions, customs celebrations, main pounds of uranium have been confirmed, that is why this economic activity, levels of poverty and education, among project is heading to be the largest lithium mine not only in others. South America but in the world [12]. On the other hand, the company Plateau Uranium highlights the importance of the social aspect in an official document published by them, in which it says that " the Social Licence represents the most Revised Manuscript Received on November 15, 2019. important step in any exploration and/or development mining * Correspondence Author Solange Ríos, Mining Engineering Program, Pontificia Universidad project in Peru" [13], for this reason " the relationships with Católica del Perú, Lima, Peru. Email: [email protected] Alexi Delgado*, Department of Engineering, Mining Engineering Section, Pontificia Universidad Católica del Perú, Lima, Peru. Email: [email protected]

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1933 & Sciences Publication

Stakeholders and Criteria on a Mining Project using AHP and Entropy-Weight Methods the Local Communities must be of a permanent nature with 2.1. Analytic Hierarchy Process (AHP) implementation as per signed and negotiated agreements " [13]. The AHP (Analytic Hierarchy Process) method was applied For this study, it was essential to identify the economic to rank the stakeholders and from there, work only with the activities developed in the area of impact of the project. For five most important. This method is based on the evaluation example, among the most important, we can mention alpaca of alternatives, taking into account several criteria, which are breeding, agriculture and tourism. All these activities involve based on the fact that the experience and knowledge of the groups of people who could be affected by the Macusani participants enjoy the same predominance as the data used in Project. In order to determine the stakeholders and the the process [17]. The values given come from the Saaty scale variables that could affect them, mathematical methods were [14]; this scale was adapted to this social context and its used that can convert qualitative data into quantitative data. structure can be seen in Table 1. In this case, the method chosen to determine the Macusani district´s stakeholders was AHP method, since it allowed us Table 1. Scale of Saaty [18] to "combine all the judgments or opinions in a whole, in Value of ajn Interpretation which the alternatives are organized from the best to the 1 j and k are equally important worst" [14]. Thus, this method was based on the evaluation of 3 j is slightly more important than k 5 j is more important than k alternatives (taking into account various criteria) and on the 7 j is strongly more important than k fact that the experience and knowledge of the experts 9 j is absolutely more important than k reflected in the surveys had the same predominance as the data used in the process [15]. It is important to mention that This study was worked with 7 alternatives (represented by this method used matrices and the Saaty scale, which stakeholders) and four criteria. The steps followed in this methodology (AHP) are detailed below [14]: provided a more complex and precise analysis than would have been obtained if a qualitative method had been used. Step 1: Criteria Comparison On the other hand, thanks to the entropy-weight method, an A criteria comparison matrix was developed with the ordering of alternatives was obtained for the general following structure [14]. This matrix is shown in Equation 1. variables that could affect these stakeholders. This methodology consists of a sequence of steps to determine the 1 푎12 … 푎1푛 entropic weights of the criteria. This was made with the 1/푎 1 … 푎 퐴 = 12 2푛 (1) purpose of ranking the variables through the multiplication of ⋮ ⋮ ⋮ ⋮ matrices. Additionally, the surveys were applied to the same 1/푎1푛 1/푎2푛 … 1 experts and using the same Saaty scale [16]. The objective of this paper was to determine both the most The sequence of this step is as follows: important stakeholders and the variables that affect them -Add the values in each column of the criteria comparison using the AHP method and the entropy-weight method. matrix -Divide each element of such matrix between the total of its In order to achieve this, it was necessary to carry out some column; the resulting matrix is called the normalized paired previous steps (specific objectives) such as, for example, comparison matrix. reviewing the existing literature of all possible -Calculate the average of the elements of each region of the methodologies that could be applied, surveying the experts relative priorities of the elements that are compared. and applying the two methodologies selected for the The purpose of these four steps is the following matrix in hierarchy of stakeholders and variables. Equation 2 [14]: The paper will present the following structure; first, in 푃′1 Section 2, both methods will be developed in a specific way, 푃′ 2 (2) in order to understand its methodology. In addition, in … ′ Section 3, the mining project that was developed in the case 푃푚 study will be explained, along with its possible stakeholders and variables. Subsequently, the results obtained will be Where m is the number of criteria and P'j is the priority of discussed in Section 4, in order to interpret them correctly criterion j with respect to the overall goal, for j = 1, 2, ..., m. and make conclusions in Section 5. In this case, the criteria, as investigated, were four.

Step 2: Stakeholders vs. Criteria comparison II. METHODOLOGY The alternative comparison matrix is developed and steps 2, 3 The methodologies to be used were AHP for the hierarchy and 4 are also applied [14]. of the stakeholders, and the entropy-weight method for the Subsequently, the sequence of this step is as follows: hierarchy of the variables. -A matrix of priorities is obtained for each alternative It is important to mention that a total of seven experts (E1, according to the criteria, as observed in Equation 3 (Annex), E2, E3, …, E7) in topics related to environment, engineering, in which Pij is the priority of stakeholder "i" with respect to mining and sociology of Peru participated. This very varied criterion "j". set of experts was very beneficial for our research work, as it allowed us to obtain more objective and real results.

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1934 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019

푃12 푃12 … 푃1푚 Step 1: Assume that there are m objects for evaluation and n 푃 푃 … 푃 evaluation criteria, which form the decision matrix Z = (z푖푗; 21 22 2푚 (3) … … … … 푖=1, 2, …, 푚; 푗=1, 2, …, 푛) [19]. 푃푛1 푃푚2 … 푃푛푚 Step 2: The decision matrix 푍= {푧푖푗; 푖=1, 2, …, 푚; 푗=1, 2, …, 푛} is normalized for each criterion Cj (j=1, 2, ..., n). The Where Pij is the priority of alternative i with respect to normalized values 푃푖푗 are calculated by the next Equation 9 criterion j, for i = 1, 2, ..., n; and j = 1, 2, ..., m. [20]. 푧 -We make a sum-product between the priorities matrix with 푃 = 푖푗 (9) 푖푗 푚 the criteria priorities vector [14]. The above mathematical 푖=1 푧푖푗 operation is shown in Equation 4. Step 3: The entropy Hj of each criterion Cj is calculated by 푃 푃 … 푃 푃′ 푃푔 the next Equation 10. 11 12 1푚 1 1 푃 푃 … 푃 푃′ 푃푔 21 22 2푚 2 = 2 (4) 퐻 = −푘 푚 푃 푙푛⁡( 푃 ) (10) … … … … … … 푗 푖=1 푖푗 푖푗 푃 푃 … 푃 ′ 푃푔 푛1 푛2 푛푚 푃푚 푛 -1 where k is a constant. k= ( log m ) Step 4: The degree of divergence divj of each criterion Cj is Where 푃푔1 is the global priority (with respect to the global goal) of the alternative i (i = 1, 2, ..., n). obtained by the next Equation 11:

Step 3: Consistency divj = 1 − Hj (11) The AHP offers a method to measure the degree of Step 5: The entropy weight Wj of each criterion Cj is consistency between the paired opinions provided by the calculated by Equation 12: 푑푖 푣 decision maker. If the degree of consistency is acceptable; 푊 = 푗 (12) 푗 푛 you can continue with the process. The steps are the 푗 =1 푑푖 푣푗 following: Step 6: The matrix Q is obtained with Equation 13 that -Matrix A nxn of paired comparisons is normalized. ij -The rows of the normalized matrix are weighted. indicates the weight that each variable has within each -We produce a product of matrices between the matrix A and criterion. the weighted one (matrix P) [14]. This matrix multiplication Qij = wj ∗ zij (13) is shown in Equation 5. Step 7: Finally, each column is added to obtain the total weight of each variable and be able to compare with the 1 푤1/푤2 … 푤1/푤푛 푤1 푛푤1 푤1 others. 푤2/푤1 1 … 푤2/푤푛 푤2 푛푤2 푤2 ⋮ = ⋮ = 푛 ⋮ (5) ⋮ ⋮ ⋮ ⋮ III. CASE STUDY 푤푛 /푤1 푤푛 /푤2 … 1 푤푛 푛푤푛 푤푛 The case to be developed focused on the Macusani mining -The column of the criteria priority matrix is added; this project, located in Puno (see Fig. 1). This project is a lithium result is called nmax. and uranium deposit, which is projected to be one of the - The consistency index is found with the following Equation largest in the world due to its high metallic content. Below is 6 [14]. the exact location of this project [21]:

푛 −푛 퐼퐶 = 푚푎푥 (6) 푛−1 Where n is the number of criteria

-The random consistency index has the following equality, as shown in Equation 7 [14].

1.98(푛−2) 퐼퐴 = (7) 푛

-The consistency ratio is found with the following Equation 8 [14].

퐼퐶 푅퐶 = (8) 퐼퐴 If: Fig. 1. Macusani Project´s Locality plan [21] RC ≤ 0.10: reasonable consistency RC > 0.10: Inconsistency Eight stakeholders and ten variables were obtained from the literature, of which only the five stakeholders and seven most 2.2. The Entropy-Weight Method important variables were determined thanks to the The entropy-weight method can be developed as follows: application of the mathematical models AHP and It is necessary for this process to use this scale to standardize entropy-weight. the answers of the surveyed.

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1935 & Sciences Publication

Stakeholders and Criteria on a Mining Project using AHP and Entropy-Weight Methods

Next, stakeholders and variables that were analyzed are possible dangers due to the new social environment, which is explained. composed of new foreign settlers.

3.1. Stakeholder groups 3.2.2. Employment (V2) According to the literature, and to the research conducted on The generation of jobs at the arrival of a new project is a economic activities and the main sources of income for the reality. This can be a favorable variable for many of the Macusani community, the eight stakeholders would be the stakeholders, since, according to the national household following: survey, the inactivity rate for Puno is 21.8% [23] .

3.1.1. Alpaca´s herdsmen (G1) 3.2.3. Health (V3) One of the most important economic activities in the In Puno, 31.92% of children die from infections in the Macusani region is the livestock of alpacas and llamas respiratory tract. This is due to the extreme cold that exists in (mostly alpacas). that area of Peru. Health, in children, adolescents and the elderly, is a factor of great importance and urgency in the 3.2.2. Local and regional government (G2) region, which is expected to improve. The provincial municipality of Carabaya (whose capital is Macusani) presents a structural organization, whose 3.2.4. Poverty level (V4) representative is Mayor Fabio Vargas Huamantuco. This In the department of Puno, the rural population represents institution uses a web page as a tool to motivate direct 46.2% [23]. Likewise, Puno is part of the Peruvian highlands, interaction with its population. and according to statistics, the rural highlands have a 49.8% poverty, higher than the 21.6% that Peru presents as a country 3.2.3. Communal authorities (G3) [22]. Therefore, the arrival of a new project could have a This group was composed of citizens from the rural areas positive impact on the economy of the region, thereby near the exploitation site, there are three very important reducing poverty rates. communities identified by the company, which are Tantamaco Community, Isivilla Community and Corani 3.2.5. Education level (V5) Community [13]. This is an important factor because, in 2017, the illiteracy rate in Puno was 10.5% [23], higher than the 9.2% presented in 3.2.4. Farmers (G4) 2016 [22]. On the other hand, if one analyses the illiteracy Agricultural activity continues to be of vital importance for rate by sex in the last 10 years in Puno, the percentage of rural areas. For this reason, it is very important to identify the illiterate women is approximately four times higher than that people who grow the main products for the region, such as of men. Thus, the percentage of illiterate women and men potatoes, alfalfa and oats [22]. was 15% and 3.6%, respectively in 2017 [24].

3.2.5. People who live on tourism (G5) 3.2.6. Water access (V6) Since Macusani is very close to the Madre de Dios basin, Since there are many lakes around the zone, many people access to water by the mine would be a latent problem for the make their living by the tourism income. community, since a tension relationship would be established

in case some body of water could be damaged by mining 3.2.6. Merchants (G6) activities. Due to the diverse agricultural products that the community owns, such as potatoes, people see trade as a main activity 3.2.7. Pollution (V7) that generates a monthly income. Predominant variable in all mining projects to be established in any region. The project has high contents of uranium, a 3.2.7. Women Spinners (G7) radioactive element, and currently, there is no legislation in Thanks to the breeding of alpacas in the area, women see an Peru to control its extraction. It is for this reason that this opportunity the spinning mill and the tissues for general a variable can be fundamental for the social viability of the secondary income. project because there is still not enough information about the effects that uranium and lithium have on the environment. 3.2.8. Local professionals (G8) They are people who reached a certain level of higher 3.2.8. Cultural Difference (V8) education and work in jobs with major responsibilities. One of the inevitable consequences of a mining project in the communities is the arrival of foreign people. A clear example 3.2. Variables of this is the growth of social demands generated in a The variables were selected according to the most common community due to the increase of its population, which problems that a region in the country can go through. Thus, generates pressure on the health sector and infrastructure the effect that the project would have on the locality can be systems [25]. measured in more real parameters. The lifestyle of local people is going to undergo a transformation, this change "creates concern, fear, hopes and 3.2.1. Security (V1) expectations and not everyone shares the same perspective, The integration of a new project generates new jobs, direct or indirect. It also generates many related activities that support mining activity. For this reason, the population is exposed to

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1936 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019 some people feel they are losing out, while others see new How a possible social conflict can impact on the opportunities" [26] . In this way, a division can be generated governmental system of the locality, of the region; even of in the community itself between those who are in favour of the the country. mining project and those who are against it. 3.1. Calculations 3.2.9. Economy (V9) Mining activity does not necessarily bring about economic Following the steps described in the methodology point, progress due to external factors that do not depend on it. In one of the examples developed using the AHP method and Peru, mining companies tax about 40% of all their utilities. In the entropy-weight method will be explained. spite of this, poor institutionality causes distribution problems, which remains one of the fundamental causes of A. THE AHP METHOD conflicts between mines and communities [27]. For this reason, it is important to identify the economic activities of Step 1: Criteria Comparison the place in order to promote it and make them grow with the From the surveys conducted and the sequence in Step 1, the help of the state. It is extremely important that action plans are criteria comparison matrices were obtained for each expert. managed, as any lack of action can have adverse consequences. For example, in Table 2, the matrix carried out for expert E3 For example, the rental price of properties is likely to increase is shown. or transport lines are likely to become congested due to the completion of the mining project. This can lead to those who do Table 2. Criteria Comparison Matrix, expert E3 not have the education or skills to participate in the boom CRITERIA COMPARISON MATRIX being left behind [26]. For this reason, these action plans CRITERIA C1 C2 C3 C4 STANDARDIZED MATRIX WEIGHT should strengthen local businesses and economies by involving C1 1 5 9 3 0.608 0.536 0.5 0.662 0.576 active community participation [28]. The above will generate C2 1/5 1 3 0.333 0.122 0.107 0.167 0.074 0.117 positive attitudes on the part of the community and, as a C3 1/9 1/3 1 0.2 0.068 0.036 0.056 0.044 0.051 consequence, support for mining projects in the area. C4 1/3 3 5 1 0.203 0.321 0.278 0.221 0.256 TOTAL 1.644 9.333 18 4.533

3.2.10. Technological development (V10) It should be noted that the experts surveyed were seven, so Lithium has become an essential element for current this matrix was carried out seven times. technological development, mainly in the field of the automotive industry. Thanks to it, the country of Bolivia is Step 2: Stakeholders vs. Criteria comparison one of the centers of attention of the world-wide industry By means the same scale, the stakeholders were compared [29]. for each established criterion. This is, according to each Through the extraction of lithium, one of the achievements of criterion, there was a matrix comparing the stakeholders the government of Evo Morales in Bolivia is the among them and the same sequence as the previous step was technological evolution. This is because the policy promoted performed. by the president strengthens the local economy of his Finally, for example, after applying Equation 4, the country, a clear example of this is the inauguration of the first stakeholders prioritization matrix for expert E3 was obtained, pilot plant of lithium-ion batteries located in the town of La this matrix is shown in Table 3. Palca in 2017 [30]. This could be replicated in Puno and, in this way, it could become the leading department not only in Table 3. Stakeholders Prioritization Matrix, expert E3 poverty reduction, but also in technological advancement. stakeholder C1 C2 C3 C4 PRIORITIZATION /criteria G1 0.307 0.18 0.281 0.256 0.278 G2 0.107 0.169 0.145 0.151 0.128 3.3. Criteria G3 0.155 0.268 0.193 0.255 0.196 The criteria were selected according to the parameters G4 0.068 0.081 0.122 0.13 0.088 established for the project. That is, taking into account the G5 0.094 0.066 0.076 0.057 0.08 G6 0.088 0.036 0.066 0.051 0.071 main activities, stakeholders, etc. G7 0.102 0.144 0.088 0.063 0.096 G8 0.079 0.056 0.03 0.036 0.063 3.3.1. Magnitude of social impact (C1) WEIGHT 0.576 0.117 0.051 0.256

The magnitude of the social impact implies the dimension that this could have, in case it is materialized, the level of By obtaining the prioritization of each stakeholder, it is violence that it can generate, the material damages, human possible to choose the five most important, according to this losses, etc. expert E3. Thus, in this case, the hierarchical order of the five main stakeholders was as follows: alpaca’s herdsmen, 3.3.2. Extension (C2) communal authorities, local and regional government, This criterion explains the arrival of the impact. It can be women spinners and farmers. local, regional, even national. According to the magnitude of It is important to mention that seven different prioritization it. matrices were obtained for each expert surveyed. In this way, in order to know the final hierarchical order of the 3.3.3. Probability (C3) stakeholders , the average of all priority values was obtained. The probability that a social conflict will occur according to each variable and stakeholder, according to the impact that the project has on them.

3.3.4. Governance (C4)

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1937 & Sciences Publication

Stakeholders and Criteria on a Mining Project using AHP and Entropy-Weight Methods

Step 3: Consistency Step 3: Calculation of entropy In order to corroborate that the experts did not contradict each To calculate the entropy, we proceed to use the Equation 10, other during the conduct of the surveys, the Consistency provided that a value is obtained for each criterion, as shown Ratio (RC) for each of them was determined. For example, in Table 8. In this case, the value of K was 1. after applying Equations 5, 6, 7 and 8, an acceptable consistency ratio was obtained for expert E3, which is shown Table 8. Values of entropy for each criterion, expert E3 in Table 4. ENTROPY C1 0.919 0.936 Table 4. The Consistency Ratio, expert E3 C2 C3 0.932 C4 0.896 A X P

2.386 IC 0.038 0.470 IA 0.990 Step 4: Calculation of divergence 0.205 RC 0.038 To calculate the value of the divergence, the Equation 11 was 1.053

n max 4.114 used, at each value of entropy previously found, as shown in Table 9.

From Table 4, the RC=0.038 < 0.10; therefore, the matrix Table 9. Values of divergence for each criterion, expert was consistent. The same procedure was developed for seven E3. experts. DIVERSITY C1 0.081 B. THE ENTROPY-WEIGHT METHOD C2 0.064 C3 0.068 C4 0.104 Step 1: Variable vs. Criteria comparison SUM 0.316 By means of the same scale, all the proposed variables were compared with each criterion. Hence, according to the Step 5: Calculation of criteria weight importance that each variable has according to each criterion, To calculate the weight of each criterion, the values of it was given a value for further analysis, see Table 5. diversity should be standardized, using the Equation 12. Table 5. Variable vs criteria comparison matrix, expert Then, multiply this value by one hundred to obtain a E3 percentage as the weight of each criterion. The results are Variables/Criteria C1 C2 C3 C4 presented in Table 10. V1 3 3 7 7 V2 5 3 3 1 V3 5 5 7 5 Table 10. Values of criteria weight for each criterion, V4 1 1 7 5 expert E3. V5 1 1 1 1 CRITERIA WEIGHT V6 1 1 1 1 C1 25.526 V7 1 3 5 7 C2 20.132 V8 5 3 5 7 C3 21.51 V9 3 3 3 5 C4 32.831 V10 3 1 3 1 SUM 28 24 42 40 Step 6: The Matrix Qij To order the variables according to the prioritization order given by the expert, it is necessary to use the Equation 13. Multiplying the value granted at the beginning by the expert, Step 2: Standardized/Normalized matrix by the weight of each criterion, the results are presented in Once the values for each variable have been obtained, all Table 11. the values are standardized, using Equation 9, as shown in Table 11. The Matrix 푸풊풋 , expert E3. Table 6. Variables/ Criteria C1 C2 C3 C4 C1 V1 76.579 60.397 150.571 229.818 517.365 Table 6. Variable vs criteria comparison normalized V2 127.632 60.397 64.53 32.831 285.39 matrix, expert E3 V3 127.632 100.662 150.571 164.156 543.02 V4 25.526 20.132 150.571 164.156 360.386 NORMALIZED MATRIX V5 25.526 20.132 21.51 32.831 100 Variables/Criteria C1 C2 C3 C4 V6 25.526 20.132 21.51 32.831 100 V1 0.107 0.125 0.167 0.175 V7 25.526 60.397 107.551 229.818 423.292 V2 0.179 0.125 0.071 0.025 V8 127.632 60.397 107.551 229.818 525.398 V3 0.179 0.208 0.167 0.125 V9 76.579 60.397 64.53 164.156 365.662 V4 0.036 0.042 0.167 0.125 V10 76.579 20.132 64.53 32.831 194.073 V5 0.036 0.042 0.024 0.025 V6 0.036 0.042 0.024 0.025 V7 0.036 0.125 0.119 0.175 Step 7: Prioritization of variables V8 0.179 0.125 0.119 0.175 V9 0.107 0.125 0.071 0.125 Finally, all values are added per variable and a final value is V10 0.107 0.042 0.071 0.025 obtained, which will represent the importance of each variable.

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1938 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019

According to expert E3, the variables with the greatest For instance, in 2016 there was an average annual population influence in decreasing order would be health (V3), cultural of 280,110 alpacas in Carabaya [22], which represents difference (V8), security (V1), pollution (V7), economy 13.78% of the total population of alpacas in Puno. (V9), poverty level (V4) and employment (V2). The above is Additionally, this economic activity generates the production shown in the Table 12. of meat and fiber in Carabaya, using 72% of its total alpaca cattle. As the previously explained, it can be shown in Table 14 [22]. Table 12. Variable prioritization matrix, expert E3

Variables/ Criteria C1 C2 C3 C4 SUM Table 14. Population of Alpaca, Production of Meat y V1 76.579 60.397 150.571 229.818 517.365 Fiber in Puno [22] Meat production Fiber production V2 127.632 60.397 64.530 32.831 285.390 Average Province population per N° of heads Tonnes Animals in shearing Tonnes V3 127.632 100.662 150.571 164.156 543.020 year Puno 175,090 21,011 567 105,054 221 V4 25.526 20.132 150.571 164.156 360.386 Azángaro 198,010 23,761 642 118,806 249 Carabaya 280110 33613 908 168066 353 V5 25.526 20.132 21.510 32.831 100.000 Chucuito 191,020 22,922 619 114,612 241 El Collao 182,340 21,881 591 109,404 230 V6 25.526 20.132 21.510 32.831 100.000 Huancané 154,420 18,530 500 92,652 195 V7 25.526 60.397 107.551 229.818 423.292 Lampa 293,080 35,170 950 175,848 369 Melgar 296,210 35,545 960 177,726 373 V8 127.632 60.397 107.551 229.818 525.398 Moho 9,860 1,183 32 5,916 12 S.A. de Putina 142,110 17,053 460 85,266 179 V9 76.579 60.397 64.530 164.156 365.662 San Román 56,630 6,796 183 33,978 71 V10 76.579 20.132 64.530 32.831 194.073 Sandia 53,160 6,379 172 31,896 67 Yunguyo 450 54 1 270 1 TOTAL 2,032,490 243,898 6,585 1,219,494 2,561 It is important to mention that seven different prioritization matrices were obtained for each expert surveyed. Thus, in 4.1.3. Local and regional government (G2) order to know the final hierarchical order of the variables, the Currently, Puno is in a critical state in its governance since average of all priority values was calculated. recently Walter Aduviri was elected as regional governor of Puno [32]. This generates a confusion in the mining companies that are carrying out projects in this area. Is is IV. RESULT AND DISCUSSION because in 2006, Aduviri staged a violent protest against the 4.1. Stakeholder analysis Santa Ana project [33]. For this reason, he was sentenced to 7 years in prison after According to Table 13, the most important stakeholders being appointed as author of the crime against public obtained by the application of the AHP method were the tranquility in his modality of crimes against public peace following: [32]. This panorama shows that the local and regional authorities Table 13. Stakeholders obtained by the AHP method are a determining stakeholder at present, since there is a lot of Stakeholder Identification uncertainty in the governmental aspect. This means that the E1 E2 E3 E4 E5 E6 E7 Results G3 0.212 0.229 0.196 0.225 0.359 0.200 0.168 0.227 mining company must carry out a sustainable social G1 0.337 0.253 0.278 0.123 0.140 0.149 0.198 0.211 management without neglecting this stakeholder. G2 0.167 0.084 0.128 0.053 0.059 0.299 0.243 0.147 G4 0.076 0.097 0.088 0.313 0.089 0.102 0.123 0.127 4.1.4. Farmers (G4) G6 0.093 0.244 0.071 0.165 0.033 0.078 0.126 0.116 G7 0.015 0.045 0.097 0.018 0.148 0.035 0.038 0.056 Another of the main economic activities of Puno is farming. G8 0.072 0.013 0.063 0.031 0.128 0.017 0.052 0.054 In the INEI´s 2017 annual report, it can observe a growing G5 0.029 0.035 0.080 0.073 0.045 0.048 0.051 0.052 trend of hectares of the areas planted and harvested during the years 2006 and 2016. The main agricultural crops that we can 4.1.1. Communal authorities (G3) mention are olluco and quinoa. The above mentioned is They were the most important in the hierarchy made by the shown in Table 15 and Fig. 2 [22]. AHP method. It is consistent because since for several years, mining companies in Puno have sought to work with them Table 15. Planted and harvested surface of principal through workshops on mining, environment, social agriculture crops in Puno [22] management, sustainable development and investment Puno: Planted and harvested surface of principal agriculture crops (2016) projects [31]. This proves that they are aware of the influence Farming Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Olluco 1,36 1,49 1,68 1,66 1,70 1,73 2,03 2,33 2,33 2,57 2,76 of this stakeholder. 27,0 28,3 30,2 32,8 32,9 34,6 36,4 Quinoa 24,02 24,60 26,26 23,38 5 6 6 2 2 4 3 Cultivated pastur 10,6 5,18 5,79 66 5,40 507 532 8,01 8,91 9,04 9,50 4.1.2. Alpaca´s herdsmen (G1) e 6 42,3 47,6 55,8 13,13 15,69 6,41 5,74 2,02 4,40 3,83 4,10 The population of this stakeholder is one of the largest in the Alfalfa 5 7 5 department of Puno, and especially in the province of Carabaya. This is demonstrated by Puno´s annual report published by the National Institute of Statistics and Informatics (INEI) in 2017. This report shows a population growth of the alpaca from 2011 to 2016, this generates that the Alpaca´s livestocks add more power or influence in their locality.

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1939 & Sciences Publication

Stakeholders and Criteria on a Mining Project using AHP and Entropy-Weight Methods

social conflicts for the development of mining projects in the Hectares cultivated department of Puno in general. 100000 80000 4.2.2. Poverty Level (V4) 60000 40000 The level of poverty of rural communities in Peru amounts to 20000 21.8%, of which, 34.6% is in Puno and 6.5% of that 0 population belong to extreme poverty [35]. The Peruvian Hectares cultivated Hectares Institute of Economics (IPE) in 2017 reported that the province of Carabaya has 50% more incidence [35], as shown in Fig. 3. This shows that it is a very important variable since Years it is what most affects the stakeholders.

Fig. 2. Hectares cultivated in the last ten years in Puno 4.1.5. Merchants (G6) The last main stakeholder is merchants, which have also been increasing since 2006 with 2,423 merchants until 2016 with 2,965 merchants [22], which represents a growth of 22.37%. It can be seen in Table 16 [22].

Table 16. Growing trend of merchants in the last ten years in Puno [22] Principal 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Characteristics Agriculture/ 4,503 4,153 4,121 4,115 4,042 4,143 4,039 4,048 4,114 4,283 4,293 Fishing/Mining Manufacture 1,403 1,599 1,634 1,605 1,641 1,548 1,627 1,590 1,507 1,502 1,542 Construction 539 613 655 738 842 866 918 975 1,014 1,044 997 Trading 2423 2527 2518 2682 2648 2789 2939 3009 3007 2890 2965 Communications 893 1,017 1,109 1,111 1,130 1,226 1,190 1,205 1,270 1,315 1,362 and Transport Other services 3,923 4,289 4,421 4,647 4,786 4,735 4,829 4,857 4,885 4,887 5,039

4.2. Analysis of the variables

On the other hand, we can analyze the variables obtained by the entropy-weight method. The total results in hierarchical order are presented in Table 17. Fig. 3. Incidence of poverty in the provinces of Puno [35] Table 17. Variables obtained by the entropy-weight method In addition, 5,423 people were registered in poverty, which Determination of variables represents 41.2% of the total population of Macusani; and E1 E2 E3 E4 E5 E6 E7 Results 987 people in extreme poverty, which represents 7.5% [36]. V 7 888.508 900 423.292 925.706 900 749.528 500 755.2907 V4 748.553 900 360.386 772.65 574.513 700 645.676 671.6824 However, it is important to mention that this district is V2 749.162 580.46 285.39 462.427 900 690.967 754.324 631.8186 incorporated into the National Multiannual System and V3 435.464 840.23 543.02 317.934 551.743 645.008 700 576.1999 Investment Management (INVIERTE.PE). In this way, this V9 627.855 654.092 365.662 215.484 637.256 700 631.913 547.4661 district has funding resources for projects that contribute to V6 748.553 240.23 100 657.957 700 709.039 478.836 519.2307 V1 620.698 341.557 517.365 270.991 533.77 771.279 365.986 488.8065 improving the living conditions of the population [36] and V5 360.045 577.046 100 125.706 500 700 787.485 450.0403 reduce the percentages of poverty. V8 423.972 377.046 525.398 168.541 437.256 700 471.099 443.3302 V10 311.492 120.749 194.073 117.128 100 300 596.987 248.6327 4.2.3. Employment (V2) The variable employment is of great importance since that is 4.2.1. Pollution (V7) the first improvement that the community of a mining According to the hierarchy, this variable is the most company directly perceives. If we analyze the case of Puno, important of all, which means that any impact made on the this region "has a high percentage of informal employment", environment, would have a very strong impact on the 85.3% is informal since only 22.6% of the PEA has at least behavior of the community. According to 2017 annual report higher education [35]. [22], it can be seen that the loss of forests per year in hectares has been increasing during the last 10 years, this causes the 4.2.4. Health (V3) population to feel dissatisfied with this aspect. Additionally, The variable Health is one of the main deficiencies of the if we add the perspective of Aduviri, the situation can be rural communities of Puno and Macusani district is not the complicated since one of the basic points of his speech is that exception. It has been recorded that the number of health of pollution mitigation. facilities (26) in the province of Carabaya has remained On the other hand, we can mention the loss of prestige of the constant despite the fact that the population has been Aruntani mine in the province of Lampa (Puno) due to the increasing [22]. pollution of the Jatun Ayllu river [34]. This generates a distrust of the peasant community and it does not favor the prestige of mining in Puno. If this variable is negatively impacted, such affectation can generate very dangerous

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1940 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019

The population most affected by these problems are children, influence zone, with the aim of reducing the social gap in Macusani, it is recorded that 29.1% of children under one reflected in the poverty index. year require additional attention monthly because they were Furthermore, the investigation of the social issues applied born with less than 2.5 kilos of weight [36]. in this mining project can be useful to establish a responsible mining sense focus on the participation and dialogue with the 4.2.5. Economy (V9) rural communities that are in the direct influence zones. This The economy is an important factor for the development of will improve the relationship between the company and the any community. For this reason, the mining company must community in the long term, which will enhance its know the main economic activities of the region and promote reputation in global markets, as well as the utilities increase them. In the case of Macusani project, the economic activities by the reduction of conflicts to maintain a constant are livestock, agriculture and commerce. production. The AHP method can be widely applied in the future social 4.2.6. Water access (V6) impact analysis for this mining project because it can receive This variable has been one of the repetitive causes of many the information given by a panel of experts according to their social conflicts that have occurred in Peru and throughout the experience in matter. It is highly recommended to make a world. The access to water that communities have is vital for hierarchization of the more relevant stakeholders that take the development of agriculture and livestock. The part in the conflict, in the same way, it prevents Yellowcake Company must inform the communities about the fair use of the water sources, or, on the contrary, the incompatibility of the information given from the experts by construction of water dams should be built. In this way, the using a ponderation of the criterion matrix. In the other hand, mining activity will not affect the economic activities of the the entropy-weight method is useful to make a quantification community. of the variables using a logarithmical function to stablish the weight of the answers. 4.2.7. Security (V1) Every single process of analysis evaluation is known to Rural communities in Peru have a low tolerance for criminal have limitations that come out afloat during the interview acts and Macusani is not the exception. It has been known with the experts and their feedback given. For example, it is that the guardians of the community have burned alive necessary to notice that the poll is given in a specific lapse criminals without the local police could do something about time and that the scene can change in a political election it. We can conclude that a negative alteration to the context. Secondly, it is important that the facilitator who communal security will be determinant for the occurrence of perform the poll can be prepare enough to elucidate all the a social conflict, if they link this fact with the beginning of the operations of the mining company. questions and doubts of the experts in order to obtain specific answer to the questions that are indicated. However, it is Finally, Table 18 shows the advantages and disadvantages important to note that the investigation refers to a study of that we perceive during the use of each methodology used to people with different worldviews like rural communities, carry out our research [37][38]. consequently the prediction of their behavior facing an impact such as a mining project cannot be a hundred percent Table 18 Advantages and disadvantages of both methods trustworthy. For this reason, it is necessary to travel and Advantage Disadvantages

l> The procedure of the information was know the place in situ to corroborate the information obtained complicated since it is necessary to work l> It was too tedious to carry out the and adjust our results. with several matrices AHP survey, because many matrices had to l> The correction of the matrix could be be completed. elaborated to avoid that the expert enters Finally, thanks to the effective determination of the into contradictions. l> The expert could easily get into stakeholders and variables of the communities of direct confusion because he only works l> Easy to operate the data since it is only with an extensive matrix. influence of the Macusani project, several future studies can Entropy-weight made with a hammer l> The correction of the matrix was be carried out. First, for example, it is possible to determine not used to verify if the expert entered into contradiction. Social impact assessment using some method like the mathematical model of grey glustering. On the other hand, V. CONCLUSION another future study that could be carried out is the Environmental Conflict Analysis (ECA) for the same project. The main stakeholders with the greatest influence in terms On the other hand, this methodological process can be of criteria of magnitude, extent, probability and governance applied in other mining projects in Peru and around the world of the conflict were correctly determined using the AHP in order to determine the probable causes that could generate Method. On the other hand, the analysis of the variables a mining conflict, and thus be able to prevent them and work applying each of the criteria in the entropy-weight method on them in advance. was important in determining which variables were going to have a significant impact on stakeholders. These results can REFERENCES be very useful to Plateau Uranium, as it will be able to 1. S. F. and L. Vergara, “Acceso a la Tierra y Planificación del identify its potential local suppliers and achieve negotiations Reasentamiento en La Granja,” CSRM Artículos ocacionales that enrich the value of the company and the community. In Desplazamento y Reasentamiento inducido por la Minería, p. 1, 2015. addition, in terms of improving the local, regional and 2. D. Arbeláez-Ruiz, “Diálogo, conflictividad y regulación de los Estudios de Impacto Ambiental en el Sistema minero,” Australia, national Peruvian economy it is fundamental that the 2015. government, company and community can add together efforts to generate a sustainable development in the project

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1941 & Sciences Publication

Stakeholders and Criteria on a Mining Project using AHP and Entropy-Weight Methods

3. ICMM, “Mining: Partnerships fpr development toolkit,” Int. Counc. Quantitative Data,” World Dev., vol. 78, pp. 401–419, 2016. Min. Met. (ICMM), London, UK., 2011. 28. D. Franks, “Evaluación del impacto social de los proyectos de 4. M. Kim, Y.-C. Jang, and S. Lee, “Application of Delphi-AHP methods recursos,” Australia, 2012. to select the priorities of WEEE for recycling in a waste management 29. G. Vásquez, “El litio y el futuro desarrollo de Bolivia,” Puno Cultura y decision-making tool,” J. Environ. Manage., vol. 128, no. 0, pp. Desarrollo, 2017. [Online]. Available: 941–948, 2013. https://punoculturaydesarrollo.blogspot.com/2017_01_01_archive.ht 5. E. S. T. Volaric, Tomislav;Brajkovic, “Selection of the Multimedia ml. [Accessed: 13-Sep-2019]. Applications for Learning using FAHP and,” pp. 44–48, 2013. 30. TeleSUR, “Noticias/América Latina: ¿Sabes cuáles son los avances 6. C. Li, K. Chen, and X. Xiang, “An integrated framework for effective tecnológicos de Bolivia?,” teleSUR- ABI- Granma- Gestión-RT- safety management evaluation: Application of an improved grey Prensidencia de Bolivia/ LP-CA, 2014. [Online]. Available: clustering measurement,” Expert Syst. Appl., vol. 42, pp. 5541–5553, https://www.telesurtv.net/news/Bolivia-Desarrollo-Tecnologico-2014 2015. 0929-0051.html. 7. T. R. Wojcik and D. Krapf, “Solid-State Nanopore Recognition and 31. Miguel Ampudia, “LÍDERES Y AUTORIDADES COMUNALES DE Measurement Using Shannon Entropy,” IEEE Photonics J., vol. 3, no. REGIÓN PUNO PARTICIPAN EN TALLER SOBRE MINERÍA,” 3, pp. 337–343, Jun. 2011. Revista Proactivo Perú, 2014. [Online]. Available: 8. C. Feng, S. Wu, and N. Liu, “A user-centric machine learning https://proactivo.com.pe/lideres-y-autoridades-comunales-de-region-p framework for cyber security operations center,” in 2017 IEEE uno-participan-en-taller-sobre-mineria/. [Accessed: 21-Nov-2018]. International Conference on Intelligence and Security Informatics: 32. El Comercio Perú, “Elecciones 2018: Walter Aduviri es electo virtual Security and Big Data, ISI 2017, 2017, pp. 173–175. gobernador regional de Puno,” 2018. [Online]. Available: 9. J. Staple-Clark, “How To Quantify Qualitative Research,” Unite For https://elcomercio.pe/peru/puno/elecciones-2018-walter-aduviri-gana Sight’s Global Team, 2019. . dor-virtual-gobierno-regional-puno-noticia-565427. [Accessed: 10. A. Delgado and I. Romero, “Social impact assessment on a 21-Nov-2018]. hydrocarbon proyect using triangular whitenization weight functions,” 33. Diario Gestión, “Puno y un gobernador clandestino: Walter Aduviri,” in 2016 IEEE Congreso Argentino de Ciencias de la Informática y Diario Gestión Política, 2018. [Online]. Available: Desarrollos de Investigación (CACIDI), 2016, pp. 1–6. https://gestion.pe/peru/politica/puno-gobernador-clandestino-walter-a 11. A. Delgado, “Why do any secondary students prefer the mathematics? duviri-246604. [Accessed: 21-Nov-2018]. A response using grey systems,” in Proceedings of the 2017 34. Diario Correo, “Creciente contaminación en ríos de Lampa y Melgar International Symposium on Engineering Accreditation, ICACIT indignan a población local,” Diario Correo, 2018. [Online]. Available: 2017, 2018. https://diariocorreo.pe/edicion/puno/creciente-contaminacion-en-rios- 12. Rumbo Minero, “2.5 millones de toneladas de litio en proyecto de-lampa-y-melgar-indignan-poblacion-local-811577/. [Accessed: Macusani, reporta Plateau,” 2018. . 21-Nov-2018]. 13. Plateau Uranium, “Macusani Project Uranium & Lithium in Peru 35. SINEACE, “Caracterización de la Region Puno,” 2017. Moving Towards 2020 Production,” 2018. 36. CEPLAN, “Información departamental, provincial y distrital de 14. G. B. Toskano Hurtado, “El Proceso De Análisis Jerárquico (AHP) población que requiere atención adicional y devengado per cápital,” como herramienta para la toma de decisiones en la selección de 2017. proveedores,” Universidad Nacional Mayor de San Marcos, 2005. 37. M. Sadeghi and A. Ameli, “An AHP decision making model for 15. A. Delgado and H. Flor, “Selection of the best air purifier system to optimal allocation of energy subsidy among socio-economic urban houses using AHP,” in 2017 CHILEAN Conference on subsectors in Iran,” Energy Policy, vol. 45, pp. 24–32, 2012. Electrical, Electronics Engineering, Information and Communication 38. M. Lihong, Z. Yanping, and Z. Zhiwei, “Improved VIKOR algorithm Technologies, CHILECON 2017 - Proceedings, 2017, vol. 2017-Janua. based on AHP and Shannon entropy in the selection of thermal power 16. J. Aznar and F. Guijarro, Nuevos métodos de valoración. Modelos enterprise’s coal suppliers,” Proc. Int. Conf. Inf. Manag. Innov. Manag. multicriterio. 2012. Ind. Eng. ICIII 2008, vol. 2, pp. 129–133, 2008. 17. J. Osorio and J. Orejuela, “EL PROCESO DE ANÁLISIS JERÁRQUICO (AHP) Y LA TOMA DE DECISIONES MULTICRITERIO. EJEMPLO DE APLICACIÓN,” Sci. Tech., vol. XIV, no. 39, pp. 247–252, 1995. 18. A. R. Hadadian and A. Rasoulian, “Using Analytic Hierarchy Process ( AHP ) for Selecting the Appropriate Country for Economic Integration ( Case of Iran ’ s Foreign Trade with OIC Countries ),” Int. Res. J. Financ. Econ., no. 162, pp. 24–32, 2017. 19. A. Delgado, “Citizen criminality assessment in lima city using the grey clustering method,” in Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017, 2017. 20. A. Delgado and I. Romero, “Applying grey systems and shannon entropy to social impact assessment and environmental conflict analysis,” Int. J. Appl. Eng. Res., vol. 12, no. 24, pp. 14327–14337, 2017. 21. P. S. Nat and S. Nupen, “Mineral Resource estimates for the Chilcuno Chico, Quebrada Blanca, Tantamaco and Isivilla deposits in the Puno District of Peru, updated to include lithium and potassium Prepared,” Bryanston, 2016. 22. INEI, “Compendio estadístico Puno 2017,” p. 464, 2017. 23. INEI CENSO, “PUBLICACIONES – Censos Nacionales 2017,” 2017. 24. INEI, “PERÚ: Tasa de analfabetismo de mujeres y hombres de 15 y más años de edad, según ámbito geográfico,” 2017. [Online]. Available: https://www.inei.gob.pe/estadisticas/indice-tematico/analfabetismo-y- alfabetismo-8036/. [Accessed: 05-Sep-2019]. 25. C. Aedo, “Evaluación del Impacto Social (EIS),” 2005. 26. S. Flynn, “Desde la Minería a la Nueva Economía 1 : Principios de Diseño para el Desempeño Social,” Cent. Soc. Responsib. Min. 27. P. A. Haslam and N. Ary Tanimoune, “The Determinants of Social Conflict in the Latin American Mining Sector: New Evidence with

Published By: Retrieval Number: C5694098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.C5694.118419 1942 & Sciences Publication